📊 RFM Analysis on E-commerce Customer Data

🧩 Problem Statement & Objective

Problem Statement:
E-commerce companies accumulate massive customer transaction data but often fail to utilize it effectively for strategic decision-making. Segmenting customers based on their purchasing behavior can improve marketing ROI, customer retention, and lifetime value.

Objectives:

• Apply RFM (Recency, Frequency, Monetary) analysis to segment customers.
• Use Pareto (80/20) Analysis to identify top contributors to revenue.
• Build a Power BI dashboard for interactive visualization and reporting.
• Enable data-driven decision-making for targeted marketing strategies.

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📁 Dataset Description

• Source: Kaggle - E-Commerce Dataset
• File Name: ecomm_data.csv
• Fields:
  • InvoiceNo, StockCode, Description, Quantity
  • InvoiceDate, UnitPrice, CustomerID, Country
• Contains transaction-level data from a UK-based online retailer over the course of a year.

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🛠️ Tools & Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Power BI – Dashboard development
• Jupyter Notebook – Analysis and documentation
• Excel – Data preparation (where applicable)

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📌 Methodology

1. Data Cleaning & Preprocessing

• Removed rows with null CustomerID and negative Quantity.
• Converted InvoiceDate to datetime and set analysis snapshot date.

2. RFM Metric Calculation

• Recency: Days since the customer’s last purchase.
• Frequency: Number of purchases.
• Monetary: Total spend.

3. Scoring & Segmentation

• Created R, F, and M scores using quantiles (1–5 scale).
• Combined scores to form RFM segments (e.g., 555 = Champions).

4. Pareto Analysis

• Identified top 20% of customers contributing ~80% of revenue.
• Visualized revenue contribution by segment and top customers.

5. Power BI Dashboard

• Built a dynamic dashboard to explore:
  • Segment-wise revenue contributions
  • Customer distribution
  • Revenue by day of the week
  • RFM Heatmap
  • Top/Bottom customers
  • Time-series trend of active customers

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📈 Key Insights

• 65%+ customers are dormant, requiring reactivation campaigns.
• Top 20% customers contribute to nearly 80% of revenue (Pareto Principle).
• Thursdays recorded the highest revenue generation.
• RFM Heatmap revealed strong clusters of high-spending frequent customers.

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📊 Power BI Dashboard Preview

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